World Model-Guided Adaptive Lookahead for Dynamic Multi-Objective Neural Routing Problems

We introduce a world-model-guided neural solver for dynamic multi-objective routing problems, where graphs change during route execution. Although neural combinatorial optimization (NCO) models can re-encode graphs after changes, training only on static instances may limit decision quality in evolving environments. Moreover, direct node selection may not fully account for the impact of alternative choices on final route quality. We establish a standardized training and evaluation framework that incorporates five event families into partially executed trajectories: node addition, node removal, attribute drift, addition with drift, and removal with drift. This enables dynamic adaptation without modifying the NCO architecture. We further propose world-model-guided adaptive lookahead, where a learned controller selectively evaluates Top-$K$ route completions at decision points that benefit from additional computation, refining node selections without predicting future events. A cost-to-go signal for each node further improves credit assignment in dynamic training. Experiments on MOTSP and MOCVRP across diverse dynamic profiles and problem sizes demonstrate improved solution quality, efficient inference, and strong cross-scale and large-scale generalization. Adaptive lookahead also improves frozen NCO policies on static instances.